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Joint network optimization in cooperative transmission networks with imperfect CSI

机译:CSI不完善的协作传输网络中的联合网络优化

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This paper investigates the power efficiency in downlink cooperative networks under spherical channel uncertainties, using a worst-case robust formulation. The aim of this paper is to jointly optimize the base station (BS) operation mode, mobile user (MU) association, and beamformings to achieve the robust network power gain. We formulate the problem as a mixed-integer non-convex problem with infinite number signal-to-interference-plus-noise ratio (SINR) and maximum transmit power constraints, which is nontrivial to solve even without this constraint. By firstly translating the uncertainty in CSI to the uncertainty in its covariance matrix, the SINR constraint spans a convex set using the Lagrangian based method. Due to the coupled integer variables, the complexity of exhaustive search grows exponentially with network size, motivating to develop low-complexity heuristic algorithms. We propose to solve the problem via a novel two-layer (TL) algorithm: inner and outer layer, successively. Specifically, the BS operation mode is determined successively in the outer layer using a Rank based Successive Search (RSS) method, while in the inner layer, an iterative Difference of Convex (DC) based algorithm is proposed to determine the MU association. Simulation results illustrate that the proposed algorithm can significantly reduce the total power consumption over existing alternatives.
机译:本文使用最坏情况的鲁棒公式研究了球形信道不确定性下下行合作网络中的功率效率。本文的目的是共同优化基站(BS)的工作模式,移动用户(MU)关联和波束成形,以实现鲁棒的网络功率增益。我们将该问题表述为具有无限个信号干扰加噪声比(SINR)和最大发射功率约束的混合整数非凸问题,即使没有此约束也很难解决。通过首先将CSI中的不确定性转换为其协方差矩阵中的不确定性,SINR约束使用基于Lagrangian的方法跨越了一个凸集。由于耦合了整数变量,穷举搜索的复杂度随网络规模呈指数增长,从而促使开发低复杂度的启发式算法。我们建议通过一种新颖的两层(TL)算法来解决该问题:内层和外层。具体地,使用基于秩的连续搜索(RSS)方法在外层中相继确定BS操作模式,而在内层中,提出基于凸(DC)迭代迭代的算法来确定MU关联。仿真结果表明,与现有方案相比,该算法可以显着降低总功耗。

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